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There has been a plethora of research on the performance of schedule and cost in construction projects. However, current research falls short in investigating the combined contribution of different construction technologies on project performance metrics. Specifically, none of the previous efforts have used integrated earned value management (EVM) parameters and utilized construction technologies to assess project schedule and cost performance. This paper fills this knowledge gap. To this end, a multistep research methodology was employed to (1) explore the utilization ratings of 13 construction technologies based on data collected from 52 industry experts; (2) rank these technologies in terms of their contribution to schedule and cost savings; (3) map the utilization ratings of construction technologies to schedule and cost variances by leveraging data from 48 construction projects with varying levels and scopes of technology integration to gauge the impact of using these technologies; and (4) develop an associated decision-support model that underwent rigorous verification under extreme conditions and sensitivity analysis. This study makes a noteworthy contribution to the existing body knowledge, revealing that the major drivers for schedule savings are building information modeling (BIM), unmanned aerial vehicles (UAVs), and augmented/virtual reality (AR/VR), while additive manufacturing (AM), digital twin (DT), and Internet of Things (IoT) are the most significant contributors to cost savings. Moreover, the study reveals strong correlations between various technologies, such as UAVs and laser scanning, geographic information system (GIS) and GPS, AR/VR and digital twins, as well as robotics/automation and AR/VR. Furthermore, the study introduces a novel model that enhances the prediction of project performance, specifically in terms of schedule and cost variations resulting from the adoption of various construction technologies. Eventually, it improves the decision-making process related to the deployment of the investigated construction technologies. [ABSTRACT FROM AUTHOR]

Historic Building Information Modelling (HBIM) plays a pivotal role in heritage conservation endeavours, offering a robust framework for digitally documenting existing structures and supporting conservation practices. However, HBIM's efficacy hinges upon the implementation of case-specific approaches to address the requirements and resources of each individual asset and context. This paper defines a flexible and generalisable workflow that encompasses various aspects (i.e., documentation, surveying, vulnerability assessment) to support risk-informed decision making in heritage management tailored to the peculiar conservation needs of the structure. This methodology includes an initial investigation covering historical data collection, metric and condition surveys and non-destructive testing. The second stage includes Finite Element Method (FEM) modelling and structural analysis. All data generated and processed are managed in a multi-purpose HBIM model. The methodology is tested on a relevant case study, namely, the church of Santa Ana in Seville, chosen for its historical significance, intricacy and susceptibility to seismic action. The defined level of detail of the HBIM model is sufficient to inform the structural analysis, being balanced by a more accurate representation of the alterations, through linked orthophotos and a comprehensive list of alphanumerical parameters. This ensures an adequate level of information, optimising the trade-off between model complexity, investigation time requirements, computational burden and reliability in the decision-making process. Field testing and FEM analysis provide valuable insight into the main sources of vulnerability in the building, including the connection between the tower and nave and the slenderness of the columns. [ABSTRACT FROM AUTHOR]

This article presents a case study with various developments of digital twinning of a sample of load tests performed on several railways bridges. The case study is located in Extremadura, South Western Spain and its aim is the generation of a validated, multi-layered information construct in the form of a digital twin as the result of a load test. This result is conceived, not only to verify the assumptions of the design of the bridge but also, to optimize future maintenance plans of the network. This particular case study is framed within a vaster European effort on digitization of the construction sector. Research and Innovation Actions within this demo case are aimed at integrating routine requirements and procedures of load tests with cutting edge digital technologies for the generation of validated virtual replica of these physical bridges. The generated twins during these load tests behaviourally match the obtained response during loading and as such, represent an ideal model for future simulations and behavioural predictions. Different data-gathering techniques and numerical models are integrated within a Common Data Environment (CDE). All efforts related to measurement, simulation, 3D modelling, assessment and validation can be wrapped up systematically for further use during regular operation of the asset. [ABSTRACT FROM AUTHOR]

The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard's levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study's results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning. [ABSTRACT FROM AUTHOR]

In the dynamic realm of digital twin modeling, where advancements are swiftly unfolding, users now possess the unprecedented ability to capture and generate geospatial data in real time. This article delves into a critical exploration of this landscape by presenting a meticulously devised workflow tailored for the creation of Level of Detail 3 (LOD3) models. Our research methodology capitalizes on the integration of Apple LiDAR technology alongside photogrammetric point clouds acquired from Unmanned Aerial Vehicles (UAVs). The proposed process unfolds with the transformation of point cloud data into Industry Foundation Classes (IFC) models, which are subsequently refined into LOD3 Geographic Information System (GIS) models leveraging the Feature Manipulation Engine (FME) workbench 2022.1.2. This orchestrated synergy among Apple LiDAR, UAV-derived photogrammetric point clouds, and the transformative capabilities of the FME culminates in the development of precise LOD3 GIS models. Our proposed workflow revolutionizes this landscape by integrating multi-source point clouds, imbuing them with accurate semantics derived from IFC models, and culminating in the creation of valid CityGML LOD3 buildings through sophisticated 3D geometric operations. The implications of this technical innovation are profound. Firstly, it elevates the capacity to produce intricate infrastructure models, unlocking new vistas for modeling digital twins. Secondly, it extends the horizons of GIS applications by seamlessly integrating enriched Building Information Modeling (BIM) components, thereby enhancing decision-making processes and facilitating more comprehensive spatial analyses. [ABSTRACT FROM AUTHOR]

Digital twins for medium span bridges – pilot project bridge Schwindegg – part 2: Asset Administration Shell The term ′digital twin′ is becoming increasingly popular in German‐speaking countries for the digital management and condition monitoring of bridges. Although standardised developments for the use of digital twins have already been initiated in industry and medicine, the first prototypes are currently being implemented in the construction industry. The digital images are typically operated in online environments that are proprietary to the project and have limited reusability. The article investigates the integration of scaling into Building Information Modelling models, which is reaching its limits due to the real‐time capability. It places existing definitions of the digital twin of the industry in the context of the construction industry. The implementation of the Isenbridge in Schwindegg as an Industry 4.0 Asset Administration Shell (AAS) is presented, building on Part 1 of this contribution. We show that an industrial, data‐driven digital twin can also be implemented technically and semantically for engineering structures. The article also presents the future challenges for evaluation and control mechanisms still to be developed for the structure. We also show a possible visualisation. The goal is to develop a syntax for the AAS' communication. [ABSTRACT FROM AUTHOR]

The metaverse is seen as a future generation of the Internet in which the virtual and the real merge into a common world. Technologies such as IoT, cloud computing, artificial intelligence or the reality–virtuality continuum underpin the metaverse and condition its evolution. This paper presents a bibliometric study on the WoS database (from 1995 to 2022) to obtain a comprehensive and non-subjective understanding of the metaverse. The study identifies the main subject areas and sources, the leading countries and authors. It also analyzes the evolution over time and the core and future research themes. Extended reality, blockchain, artificial intelligence and sensors are identified as core themes, while building information modeling, digital twins and governance emerge as future themes. Based on the bibliometric study, a general layered metaverse architecture is proposed that streamlines open challenges in the metaverse to assist researchers and companies in introducing innovative and disruptive improvements. [ABSTRACT FROM AUTHOR]

The emergence of Digital Twin (DT) technology presents unique opportunities for society by facilitating real-time data transfer from the physical environment to its digital counterpart. Although progress has been made in various industry sectors such as aerospace, the Architecture, Engineering, Construction, and Operation (AECO) sector still requires further advancements, like the adoption of these technologies over traditional approaches. The use of these technologies should become standard practice rather than an advanced operation. This paper aims to address the existing gap by presenting a comprehensive framework that integrates technologies and concepts derived from purpose-driven case studies and research studies across different industries. The framework is designed to provide best practices for the AECO sector. Moreover, it aims to underscores the potential of DT for optimization through overseeing and digital management of the built environment across the entire life cycle of facilities, encompassing design, construction, operation, and maintenance. It is based on an extensive literature review and presents a holistic approach to outlining the roles of Building Information Modelling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and other key enablers within the DT environment. These digital tools facilitating the simultaneous evaluation of associated benefits, such as resource savings and future prospects, like monitoring project sustainability objectives. [ABSTRACT FROM AUTHOR]

Large volumes of 3D parametric datasets, such as building information modeling (BIM), are the foundation for developing and applying smart city and digital twin technologies. Those datasets are also considered valuable tools for efficiently managing rebuilt structures during the operation and maintenance stages. Nevertheless, current approaches developed for the scan-to-BIM process rely on manual or semi-automatic procedures and insufficiently leverage semantic data in point clouds. These methods struggle to accurately represent large-scale indoor complex layouts and extract details from irregular-shaped unstructured elements, causing inefficiencies in BIM model generation. To address these issues, we propose an innovative scan-to-BIM framework based on deep learning algorithms and raw point cloud data, enabling the automatic generation of 3D models for both structured and unstructured indoor elements. Initially, we propose an enhanced deep learning neural network to improve the point clouds' semantic segmentation accuracy. Subsequently, an efficient workflow is developed to reconstruct 3D building models of structured indoor scenes. The proposed workflow can reconstruct large-scale data with multiple room layouts of Manhattan or non-Manhattan structures and reconstruct 3D models automatically by using a BIM parametric algorithm implemented in Revit software. Moreover, we introduce a robust method for unstructured elements to automatically generate corresponding 3D BIM models, even when the incorporating semantic information is incomplete. The proposed approach was evaluated on synthetic and real data for different scales and complexities of indoor scenes. The results of the experiments demonstrate that the improved model significantly enhances the overall semantic segmentation accuracy compared to the baseline models. The proposed scan-to-BIM framework is efficient for indoor element 3D reconstruction, achieving precision, recall, and F-score values ranging from 96% to 99%. The generated BIM models are competitive with traditional methods regarding model completeness and geometric accuracy. • We propose an innovative scan-to-BIM framework based on deep learning algorithms: • We propose an enhanced deep-learning neural network to improve the point clouds' semantic segmentation accuracy. • A novel and efficient workflow is developed to reconstruct 3D building models of structured indoor scenes. It can reconstruct large-scale data with multiple room layouts in 3D model automatically by using a BIM parametric • For unstructured elements, we propose a robust method to automatically generate 3D BIM models, even when the semantic information is incomplete. [ABSTRACT FROM AUTHOR]

Digital twins have emerged as highly valuable tools for model-based planning, simulation and optimization over the last couple of years, thereby demonstrating considerable potential for application within the construction industry. The introduction of building information modeling (BIM) has effectively established a standardized approach to representing building models. However, in practice, many of these models currently exhibit limitations as to their quality, specifically concerning the level of detail they encompass. In addition, BIM models too often are locked inside a specific vendor’s tool which readily implies a lack of platform independence, or interoperability, which, however, is essential for facilitating single and regressive, i.e., after a design change, model-based building performance simulations. Model-based engineering has effectively addressed comparable challenges within the domain of software engineering over the past decades by facilitating the integration and interoperability of models from various origins, by capitalizing on model-based tool integration. Prompted by these advantages, this study introduces a model-based tool environment that addresses the aforesaid challenges concerning BIM model quality and interoperability. Taking advantage of our proposed model-based tool environment, we implement an agile, continuous planning process for regressive, model-based building performance simulations, thereby enhancing building energy efficiency planning. [ABSTRACT FROM AUTHOR]

In the fight against climate change, and to improve its sustainability standards, the building sector is trying to reduce its energy consumption and environmental footprint. Leveraging digitalization, in the wake of Industry 4.0 and 5.0, holds immense potential in revolutionizing the buildings and construction sector. Digitalization is transforming buildings throughout their life cycle encompassing design, construction, operation, and retrofitting/decommissioning stages. This study aims to analyze the role of digital technologies in sustainable buildings. In doing so, a systematic literature review is conducted to examine the current landscape of the application of 13 key digital technologies - sensors, Building information modeling (BIM), Building Management Systems (BMS), smart meters, 3D printing/additive manufacturing, robotics, big data analytics, Internet of Things (IoT), machine learning, artificial intelligence, digital twin, blockchain, and cyber security - in terms of their scope and prospects in the building sector. These technologies have been discussed considering their functions, applications in buildings, adoption and market trends, challenges, and the way forward. The findings of the study reveal that digital technologies are making buildings more integrated, flexible, energy-efficient, smart, and sustainable by optimizing resource utilization, enhancing operational efficiency, and minimizing environmental impact. For instance, sensors enable real-time data collection, BIM facilitates sustainability assessments, and AI enhances energy management. Further, digital technologies can lead to a 30%–50% improvement in energy intensity in the building sector by 2040. While digital technologies offer promising solutions in terms of improving sustainability standards in buildings, they also face several challenges such as high initial cost, privacy and security risks associated with data breaches and cyber-attacks, and lack of customer awareness and a skilled workforce. Conducive policies and regulations are critical for creating an enabling environment that supports the adoption of digital technologies. Application domains of different digital technologies across a building's lifecycle. [Display omitted] • Explores the contribution of digitalization to sustainable buildings. • Discusses 13 key digital technologies for buildings in terms of functions, applications, and market trends. • Examines the prospects of digital technologies in buildings in terms of opportunities and challenges. • Makes recommendations to improve digitalization in buildings. [ABSTRACT FROM AUTHOR]

Accurate and efficient inspection of rebar dimensions has proven to be a persistent challenge for researchers and practitioners. This paper introduces a semantically enriched 3D model-based system that employs computer vision and deep learning for location-aware identification and tracking of rebar issues. The system comprises four modules: (A) digital twin generation, (B) segmentation, (C) inspection, and (D) issue identification and tracking. The generation module constructs 3D models from rebar structures. The segmentation and inspection modules analyze the 3D models, enriching them with semantic information. The issue identification and tracking module exchanges information between the semantically enriched 3D models and the building information models across time. An experiment on a column rebar cage is conducted. A precision of over 90% and a recall of over 97% are reported in 3D instance segmentation. Diameter inspection achieves an accuracy of 95.5% for large-size rebars. Spacing inspection achieves a mean relative error of 0.98%. The defective spacing is identified and tracked. • Fuses deep learning and computer vision to construct 3D semantic rebar model. • BIM- and digital-twin-based information system fulfills rebar quality control. • Autonomous rebar inspection pipeline directly generates issue reports from images. [ABSTRACT FROM AUTHOR]

BIM-to-Metaverse offers benefits to the AEC industry, but the challenge of efficiently and effectively implementing it will be formidable and require several years. The objective of this study is to identify the outsourcing forms of this new technology for construction project stakeholders and to explore its associated implementation challenges. Specifically addressed will be (a) innovations in XR technologies and how to convert BIM models to XR models for visualization in the metaverse, (b) an integrated definition function (IDEF0) model that describes how to prepare the BIM model for metaverse, (c) a use case for utilizing metaverse in the AEC industry and an evaluation of the concerns that must be resolved prior to successful implementation, and (d) an example illustrating the process. The metaverse implementation in design and construction can enhance collaboration, productivity, and quality but implementation cost, data security, and hardware and software interoperability are major challenges in its implementation. • Metaverse has potential for improving workflow and productivity in AEC industry. • This study proposed an IDEF0 model for design and construction phases. • This study explored challenges and limitations of implementing Metaverse in construction projects. • This study reviewed applications of metaverse in non-AEC industries. • This study proposed a use case for metaverse in a construction project. [ABSTRACT FROM AUTHOR]

The transformation of dynamic supply chains within the realm of offsite manufacturing presents a unique and compelling opportunity to effectively respond to shifts in market demand. However, this paradigm shift also entails novel challenges and prospects for streamlining processes, augmenting efficiency, and ultimately driving success across production workflows and logistical operations. Dynamic supply chains necessitate unimpeded access to high-quality, dependable data to facilitate real-time, data-driven decision-making. Furthermore, owing to their inherently intricate nature, these supply chains are subject to a heightened vulnerability to disruptions and delays, surpassing those encountered in traditional supply chains. Against this backdrop, this paper proposes a framework that synthesises the convergence of the Internet of Things (IoT), Building Information Modelling (BIM), and blockchain technology (BCT) into a cohesive and integrated system, thereby optimising the efficiency and transparency of supply chain management operations. The proposed framework takes cognisance of the challenges inherent in ensuring information traceability, mitigating fragmented information, enhancing interoperability, and fortifying reliability throughout the design, production, and implementation phases of assets within dynamic supply chains operating in the offsite manufacturing domain. To illustrate the efficacy of our proposition, we present a framework that exemplifies the seamless integration of a permissioned, open-source distributed ledger, which forms the bedrock of our support infrastructure for IoT and BIM integration. Through rigorous analysis, we showcase the effectiveness of the developed framework in the form of a designed Supply Chain Management Model (SCMM-PDO), which harmonises the physical, digital, and operational layers within a robust system architecture (BCT.IoT-BIM). The integration of these technologies provides a decentralised solution, reinforcing supply chains with improved security, reliability, and quality control mechanisms, thus enhancing the efficiency of the supply chain network. It is anticipated that this transformative integration will yield substantial improvements in key performance indicators (KPIs) such as operational efficiency, cost reduction, and the cultivation of trust and accountability across supply chain activities. The research proposes an IoT and BIM conceptual framework that simulates the dynamic supply chain operations, facilitating real-time analysis and prediction of supply chain participants' behaviours from resources, processes, and assets. Furthermore, the integration of IoT-BIM (digital twinning) with a blockchain-based system, such as Hyperledger Fabric, engenders a secure and transparent framework for the diligent tracking of assets and information throughout the supply chain. The findings of this study offer an all-encompassing conceptual framework for the secure and transparent tracking of asset information by integrating Blockchain-based technologies with IoT and BIM. This research endeavour thus expands the existing body of knowledge, establishing a foundation for future investigations within this proliferating field. • "Developed a real-time analysis and prediction framework for dynamic supply chain operations, integrating IoT, BIM, and blockchain technology." • "Created a supply chain management model (SCMM-PDO) integrating physical, digital, and operational layers in a robust system architecture." • "Addressed information traceability and reliability challenges in offsite manufacturing supply chain with a new framework." • "Combined IoT, BIM, and blockchain for improved efficiency and transparency in supply chain management." • "Highlight potential improvements and trust across supply chain activities through IoT, BIM, and blockchain integration." [ABSTRACT FROM AUTHOR]

New-generation intelligent construction places higher demands on digitisation and intelligence of tunnel. Digital twin (DT) effectively supports high-fidelity modelling, virtual-real mapping, and analysis-based decision-making but with research in the initial stage. To begin with, this paper delves into the complexity and uncertainty inherent in tunnel construction, highlighting DT as a promising solution compared to exisiting technologies such as Building Information Modelling. Then, a systematic literature survey is conducted, revealing growing focus on DT research topics. To provide comprehensive insights into DT-related technologies and their application in tunnel construction, this paper clusters literature from perspectives of sensor networks, Internet of Things (IoT), computer vision-based twin data acquisition, communication, natural language processing (NLP), automatic control-based connection, and geometric, semantic, analytical integrated twin modelling. These aspects shed light on potentials and limitations of existing researh in developing a functional DT. In response to the challenges of information richness, timeliness, and analytical capabilities, an improved conceptual framework tailored for tunnel is proposed to close the information and control loop. Finally, the paper discusses the prospects and gaps of DT in theory and practice to leverage DT implementation. • Digital Twin greatly enhances the development of intelligent tunnel construction. • A comprehensive literature analysis is conducted to assess status of Digital Twin technologies in tunnel construction. • A close-loop conceptual framework of Digital Twin is proposed. • Gaps and future prospects of Digital Twin for tunnel construction are discussed. [ABSTRACT FROM AUTHOR]

Digital twin, a technology bridging the physical and digital domains, has found extensive application in digitally advanced industries. However, its adoption in the construction sector remains limited, hindered by challenges related to construction, integration with real-time data capture, and visualisation platforms. This paper presents a construction industry digital twin that combines Building Information Modelling-based data visualisation with an Internet of Things-driven live data capture platform, outlining a methodology for its development. The digital twin was implemented in a university library embodying the 'Living Lab' concept to account for the nuances associated with live environments. It integrates sensors with Building Information Modelling to offer a semiotic representation of the library's internal conditions in digital twin, empowering facility managers to proactively optimize thermal, lighting, and air quality. • Digital twin (DT) presents opportunities to proactively address challenges before they occur. • Complexities with the building of DT in the construction industry pose many difficulties. • An appropriate data capture and visualisation platform is key to DT development. • Application of DT in educational buildings, specifically in 'Living Labs' is scarce. [ABSTRACT FROM AUTHOR]